The volume of data recordable on an optical recording medium generally increases as the density and the number of layers of the medium increase. On the other hand, this makes it difficult to secure accuracy in a recording condition optimization process. Therefore, the amount of nonlinear noise component (e.g., a vertically asymmetrical component) remaining in a reproduction signal from the optical recording medium increases. This nonlinear noise component remaining in the reproduction signal may cause a bit error and disturb the reproduction of user data. An optical recording medium reproduction apparatus typically uses, e.g., an FIR (Finite Impulse Response) filter in order to suppress noise components remaining in a reproduction signal. Since, however, the FIR filter is used to implement a linear equalization process, it is difficult for the FIR filter to sufficiently suppress the nonlinear noise component.
To suppress the nonlinear noise component remaining in a reproduction signal, therefore, it is desired that a nonlinear equalization process be performed on the reproduction signal. Various filters have been proposed to implement this nonlinear equalization process. For example, a neural network filter, Volterra filter, ARML (Auto Regressive Maximum Likelihood) filter, and decision feedback ML filter can implement the nonlinear equalization process.
Unfortunately, a plurality of types of nonlinear noise components are mixed in a reproduction signal from a high-density multilayered optical recording medium. Even when singly using any of the above-mentioned filters, therefore, it is difficult to sufficiently suppress the nonlinear noise components and stably perform adaptive control of the filter.